Is your e-commerce store getting the most value from the data you collect? Ideally, your store's digital marketing program uses data to personalize each customer's experience. Your store's fraud control program is likely also using data to authenticate your customers at checkout. But are these two data sets connected?
For many retailers, they're not – and the disconnect can undermine customer experience (CX) efforts, revenue and customer retention. All the money your store spends to customize touchpoints and serve personalized recommendations to your customers is wasted if your fraud control program doesn't recognize those same customers at checkout and treats their orders as fraud.
Rejection of good orders, known as false declines or false positives, is something nearly a quarter of online shoppers across five countries (the U.S, U.K., Canada, Mexico and Australia) have experienced, according to a March 2020 Sapio Research survey commissioned by ClearSale. This damages CX, sometimes beyond repair.
The stakes are rising for delivering great CX and preventing e-commerce fraud. Despite the challenges retailers and shippers face because of the pandemic, 80% of consumers said they expected better customer service in 2020. Meanwhile, digital fraud increased by 55% from March through the end of 2020, putting e-commerce revenue at risk.
Many merchants already have the key to avoiding CX errors and fraud in their data, but they haven't fully tapped its power yet. Here's why and how retailers can use the data more effectively to close the CX gap between marketing and fraud control.
Even before the pandemic pushed people to rely more on e-commerce, 80% of consumers said they were more likely to buy from brands that gave them a personalized experience. Now, with customer expectations higher than ever – and more competition among online retailers – personalization at each touchpoint is the key to customer engagement.
Nothing breaks the experience of feeling recognized like being rejected, but more than 27% of U.S. shoppers in the Sapio survey said they'd had an online order declined. When that happens, customers may feel frustrated and insulted – and they certainly don't feel recognized.
In fact, the Sapio survey found that customers are much less tolerant of false declines than they are of fraud. Thirty-three percent of U.S. shoppers surveyed said they would never shop again with a merchant after a decline, and 25% would complain about the experience on social media. Yet only 19% said they would abandon a merchant after having a fraud experience with them.
Those statistics represent a lot of lost marketing spend and brand damage – problems that can be prevented with better use of customer data for fraud prevention.
Fraud prevention requires data to authenticate customer identities, not just payment methods. With billions of records exposed due to data breaches, even careful consumers can become the victims of not only card theft but also account takeover (ATO) fraud that's harder for basic fraud-screening tools to detect.
To avoid chargebacks due to ATO fraud, retailers need to make sure they can verify each customer by characteristics that fraudsters find hard to mimic. That can include behavioral biometric data – how hard the customer typically taps their smartphone screen, for example – as well as past purchases, typical behavior on the store website, and other indicators that the person placing the current order is the same customer who's placed good orders in the past.
For example, let's say a customer wants to order $500 of auto parts from an online store. They signed in with their established customer account and are using their default payment method. A good fraud-screening program will raise a flag if this customer has only placed $50 orders for car wash supplies in the past – especially if this is the first time they've visited the auto parts section of the website.
However, it would be a mistake for the merchant to stop here and reject the order. Instead, manual reviewers can analyze the order more thoroughly before making a decision. What if the customer is visiting family and has offered to help a relative restore a car that has sentimental meaning? Perhaps they placed their order on their phone after browsing Instagram for pictures of restored cars and getting served ads based on those searches.
How are they going to react if they've followed their customer journey to its logical conclusion, only to be turned down? Only 35% of U.S. consumers told Sapio they'd try again after having a payment declined – and remember, 33% said they'd never shop with a merchant again after a decline.
The risk of false declines is one reason we recommend that merchants manually review all flagged orders instead of automatically rejecting them. An experienced fraud analyst could review all the available data and quickly conclude that the order was legitimate. The customer would get their purchase. The merchant would get revenue, a better return on their marketing spend, and a continued relationship with the customer.
Manual review also creates data that makes the order-screening process more efficient over time. As analysts review orders, they can feed their decisions into the automated fraud-screening AI engine. Then the machine can learn to identify patterns that indicate good customers exhibiting unusual but legitimate behavior, as opposed to account takeovers and other types of fraud. That makes the whole system smarter, more efficient and less prone to errors that can turn customers away.
Using customer data behind the scenes can also reduce cart abandonment by shifting the burden of authentication from the shopper to the merchant. While 92% of U.S. consumers in the Sapio survey said security is very important to them when they shop online, 50% said they'd abandoned purchases because the checkout process was too long or complicated, and 38% said they'd abandoned purchases because the merchant required them to create an account.
The takeaway here is that the more data you require customers to enter before they can make a purchase, whether it's a password or a two-factor authentication code or something else, the more likely they are to leave and go to another store with a faster, easier checkout. Leveraging your data allows you to identify your customers while delivering a convenient, welcoming experience – without leaving your store open to fraud.
What kinds of data can work for both fraud control and marketing? As in the car-part example above, biometric, geolocation, device, past purchases and site behavior, and social data all have roles to play in fraud prevention. Location and social data are also important for personalization, and customer responses to email and paid marketing campaigns can help create a clearer picture of your customers, too.
Of course, this data can only help you streamline your checkout process and approve more good orders if it's accessible to your fraud control program. If your marketing and fraud prevention data lives in separate silos, it's time to unify that data so that you have a single view of your customers for your marketing and fraud control teams. This takes some work, but the result can be more completed purchases, more approved orders and more satisfied customers, all while stopping fraudsters.
Original article at: https://www.business.com/articles/unify-customer-data/